The overall goal of this project is to develop an integrated single-trial system for neuroimaging which combines high-density electroencephalography (EEG) with simultaneous functional magnetic resonance imaging (fMRI), and to use this system to investigate variability in neural processing. The high-temporal resolution of EEG will enable the detection of signal variability in single-trial events, and this information will be used as the input function for analysis of simultaneously acquired event-related fMRI (efMRl). We hypothesize that using single-trial EEG derived regressors for efMRI (stEEG/fMRI) will yield high spatial and high temporal resolution information about the functional neuroanatomy involved in cognitive processing. This will enable construction of unique EEG derived tMRI activation maps which are not based on pre-defined labels or observed behavioral responses but rather on task and subject specific electrophysiological source variability. The broad impact of this work will be development of a new non-invasive imaging system (stEEG/fMRI) for the cognitive neurosciences as well as a clinical tool for diagnosis and monitoring of a broad spectrum of neurological diseases. The R21 effort focuses on development of a high density (64 channels) EEG/fMRI integrated system for single-trial analysis, and characterization of possible differences between the EEG recorded in an MR environment and that recorded in a standard environment. The R33 will then demonstrate the use of stEEG/fMRI in a pilot study of cognitive aging. R21Aims: 1. Develop an in-magnet 64 channel EEG system for single-trial analysis of event-related potentials recorded concurrently with fMRI. 2. Assess the quality of EEG collected inside the MR scanner compared to that collected in a shielded EEG room, using a series of predefined protocols for characterizing the effects of the auditory and magnetic environments on EEG and ERP wave forms. 3. Validate that EEG recorded simultaneously with fMRI is of a high enough quality to detect task relevant single-trial signatures using supervised machine learning. R33 Aims: 1.Use single-trial EEG-derived regressors, constructed via supervised machine learning, to construct efMRI activation maps (stEEG/fMRI activation maps) for auditory oddball and Eriksen flanker tasks. 2.Use alpha power as a complementary regressor within stEEG/fMRI for capturing additional single-trial variance in the hemodynamic response. 3.Demonstrate that stEEG/tMRI activations maps yield new information for discriminating young and old adult populations, as compared to traditional efMRI and P3 and ERN ERP analysis.